Adjuvant Therapy After Upfront Resection of Resectable Pancreatic Cancer: Patterns of Omission and Use—A Prospective Real-Life Study
Bibliographic record
Abstract
BACKGROUND: Little is known about adjuvant therapy (AT) omission and use outside of randomized trials. We aimed to assess the patterns of AT omission and use in a cohort of upfront resected pancreatic cancer patients in a real-life scenario. METHODS: From January 2019 to July 2022, 317 patients with resected pancreatic cancer and operated upfront were prospectively enrolled in this prospective observational trial according to the previously calculated sample size. The association between perioperative variables and the risk of AT omission and AT delay was analyzed using multivariable logistic regression. RESULTS: Eighty patients (25.2%) did not receive AT. The main reasons for AT omission were postoperative complications (38.8%), oncologist's choice (21.2%), baseline comorbidities (20%), patient's choice (10%), and early recurrence (10%). At the multivariable analysis, the odds of not receiving AT increased significantly for older patients (odds ratio [OR] 1.1, p < 0.001), those having an American Society of Anesthesiologists score ≥II (OR 2.03, p = 0.015), or developing postoperative pancreatic fistula (OR 2.5, p = 0.019). The likelihood of not receiving FOLFIRINOX as AT increased for older patients (OR 1.1, p < 0.001), in the presence of early-stage disease (stage I-IIa vs. IIb-III, OR 2.82, p =0.031; N0 vs. N+, OR 3, p = 0.03), and for patients who experienced postoperative major complications (OR 4.7, p = 0.009). A twofold increased likelihood of delay in AT was found in patients experiencing postoperative complications (OR 3.86, p = 0.011). CONCLUSIONS: AT is not delivered in about one-quarter of upfront resected pancreatic cancer patients. Age, comorbidities, and postoperative complications are the main drivers of AT omission and mFOLFIRINOX non-use. CLINICALTRIALS REGISTRATION: NCT03788382.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".